Overview

Dataset statistics

Number of variables14
Number of observations2778
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory304.0 KiB
Average record size in memory112.0 B

Variable types

Numeric14

Alerts

gross_revenue is highly correlated with qnt_purchases and 3 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
var_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with avg_basket_varietyHigh correlation
avg_recency_days is highly correlated with freq_purchaseHigh correlation
freq_purchase is highly correlated with avg_recency_daysHigh correlation
qtd_returned is highly correlated with freq_returnsHigh correlation
freq_returns is highly correlated with qtd_returnedHigh correlation
avg_basket_size is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_basket_variety is highly correlated with var_products and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qnt_purchases and 1 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
var_products is highly correlated with qnt_purchasesHigh correlation
qnt_items is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_ticket is highly correlated with qtd_returned and 1 other fieldsHigh correlation
qtd_returned is highly correlated with avg_ticket and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticket and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qnt_purchases and 2 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
var_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_recency_days is highly correlated with freq_purchaseHigh correlation
freq_purchase is highly correlated with avg_recency_daysHigh correlation
qtd_returned is highly correlated with freq_returnsHigh correlation
freq_returns is highly correlated with qtd_returnedHigh correlation
avg_basket_size is highly correlated with qnt_itemsHigh correlation
df_index is highly correlated with avg_recency_daysHigh correlation
gross_revenue is highly correlated with qnt_purchases and 5 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
var_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 5 other fieldsHigh correlation
avg_ticket is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_recency_days is highly correlated with df_indexHigh correlation
qtd_returned is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 51.93815733) Skewed
freq_purchase is highly skewed (γ1 = 46.11842612) Skewed
qtd_returned is highly skewed (γ1 = 50.13804465) Skewed
avg_basket_size is highly skewed (γ1 = 44.89224702) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 34 (1.2%) zeros Zeros
qtd_returned has 1484 (53.4%) zeros Zeros
freq_returns has 1484 (53.4%) zeros Zeros

Reproduction

Analysis started2021-10-16 22:44:17.286109
Analysis finished2021-10-16 22:44:36.619234
Duration19.33 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct2778
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2254.548956
Minimum0
Maximum5706
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-16T19:44:36.693909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile181.85
Q1903.5
median2063.5
Q33415.25
95-th percentile4964.65
Maximum5706
Range5706
Interquartile range (IQR)2511.75

Descriptive statistics

Standard deviation1528.642631
Coefficient of variation (CV)0.6780259205
Kurtosis-0.9556736234
Mean2254.548956
Median Absolute Deviation (MAD)1242
Skewness0.3793762679
Sum6263137
Variance2336748.294
MonotonicityStrictly increasing
2021-10-16T19:44:36.780861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
29121
 
< 0.1%
28981
 
< 0.1%
29011
 
< 0.1%
29021
 
< 0.1%
29061
 
< 0.1%
29071
 
< 0.1%
29081
 
< 0.1%
29091
 
< 0.1%
29111
 
< 0.1%
Other values (2768)2768
99.6%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57061
< 0.1%
56961
< 0.1%
56901
< 0.1%
56651
< 0.1%
56591
< 0.1%
56481
< 0.1%
56471
< 0.1%
56301
< 0.1%
56291
< 0.1%
56201
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2778
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15284.58855
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-16T19:44:36.871455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12625.85
Q113815.25
median15240.5
Q316778.75
95-th percentile17950.15
Maximum18287
Range5940
Interquartile range (IQR)2963.5

Descriptive statistics

Standard deviation1714.871225
Coefficient of variation (CV)0.1121960999
Kurtosis-1.205850392
Mean15284.58855
Median Absolute Deviation (MAD)1482.5
Skewness0.01613485486
Sum42460587
Variance2940783.317
MonotonicityNot monotonic
2021-10-16T19:44:36.961924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
141631
 
< 0.1%
177041
 
< 0.1%
169331
 
< 0.1%
137721
 
< 0.1%
162491
 
< 0.1%
141981
 
< 0.1%
139891
 
< 0.1%
179301
 
< 0.1%
144821
 
< 0.1%
Other values (2768)2768
99.6%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123631
< 0.1%
123641
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182651
< 0.1%
182631
< 0.1%
182611
< 0.1%
182601
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2764
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2901.74969
Minimum36.56
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-16T19:44:37.055211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum36.56
5-th percentile264.593
Q1627.5425
median1167.815
Q32423.24
95-th percentile7569.3135
Maximum279138.02
Range279101.46
Interquartile range (IQR)1795.6975

Descriptive statistics

Standard deviation10919.63813
Coefficient of variation (CV)3.763122012
Kurtosis332.4150944
Mean2901.74969
Median Absolute Deviation (MAD)685.71
Skewness16.2721489
Sum8061060.64
Variance119238497
MonotonicityNot monotonic
2021-10-16T19:44:37.142192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1025.442
 
0.1%
731.92
 
0.1%
1314.452
 
0.1%
1353.742
 
0.1%
2092.322
 
0.1%
734.942
 
0.1%
178.962
 
0.1%
889.932
 
0.1%
379.652
 
0.1%
2053.022
 
0.1%
Other values (2754)2758
99.3%
ValueCountFrequency (%)
36.561
< 0.1%
521
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
70.021
< 0.1%
77.41
< 0.1%
84.651
< 0.1%
90.31
< 0.1%
93.351
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%

recency_days
Real number (ℝ≥0)

ZEROS

Distinct252
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.73650108
Minimum0
Maximum372
Zeros34
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-16T19:44:37.234345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median29
Q373
95-th percentile211
Maximum372
Range372
Interquartile range (IQR)63

Descriptive statistics

Standard deviation68.43847364
Coefficient of variation (CV)1.20625122
Kurtosis3.407506126
Mean56.73650108
Median Absolute Deviation (MAD)24
Skewness1.891960465
Sum157614
Variance4683.824674
MonotonicityNot monotonic
2021-10-16T19:44:37.332970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.6%
487
 
3.1%
285
 
3.1%
385
 
3.1%
876
 
2.7%
1067
 
2.4%
966
 
2.4%
765
 
2.3%
1762
 
2.2%
2255
 
2.0%
Other values (242)2031
73.1%
ValueCountFrequency (%)
034
 
1.2%
199
3.6%
285
3.1%
385
3.1%
487
3.1%
543
1.5%
765
2.3%
876
2.7%
966
2.4%
1067
2.4%
ValueCountFrequency (%)
3721
 
< 0.1%
3661
 
< 0.1%
3601
 
< 0.1%
3583
0.1%
3541
 
< 0.1%
3371
 
< 0.1%
3362
0.1%
3341
 
< 0.1%
3332
0.1%
3301
 
< 0.1%

qnt_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.04787617
Minimum2
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-16T19:44:37.429961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q36
95-th percentile17
Maximum206
Range204
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.066088572
Coefficient of variation (CV)1.499053274
Kurtosis184.1539699
Mean6.04787617
Median Absolute Deviation (MAD)2
Skewness10.63032214
Sum16801
Variance82.193962
MonotonicityNot monotonic
2021-10-16T19:44:37.528252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2783
28.2%
3500
18.0%
4393
14.1%
5237
 
8.5%
6173
 
6.2%
7138
 
5.0%
898
 
3.5%
969
 
2.5%
1055
 
2.0%
1154
 
1.9%
Other values (45)278
 
10.0%
ValueCountFrequency (%)
2783
28.2%
3500
18.0%
4393
14.1%
5237
 
8.5%
6173
 
6.2%
7138
 
5.0%
898
 
3.5%
969
 
2.5%
1055
 
2.0%
1154
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

var_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct467
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.6166307
Minimum2
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-16T19:44:37.628556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q134
median72
Q3143
95-th percentile399.45
Maximum7838
Range7836
Interquartile range (IQR)109

Descriptive statistics

Standard deviation277.6059198
Coefficient of variation (CV)2.141746151
Kurtosis337.2390574
Mean129.6166307
Median Absolute Deviation (MAD)45
Skewness15.35760391
Sum360075
Variance77065.04671
MonotonicityNot monotonic
2021-10-16T19:44:37.939227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2838
 
1.4%
3534
 
1.2%
2730
 
1.1%
2630
 
1.1%
2930
 
1.1%
3128
 
1.0%
1527
 
1.0%
1927
 
1.0%
2527
 
1.0%
3326
 
0.9%
Other values (457)2481
89.3%
ValueCountFrequency (%)
211
0.4%
313
0.5%
416
0.6%
516
0.6%
624
0.9%
714
0.5%
813
0.5%
919
0.7%
1019
0.7%
1123
0.8%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16371
< 0.1%

qnt_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1639
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1698.637149
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-16T19:44:38.037320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile119.85
Q1330.25
median703.5
Q31478
95-th percentile4639.5
Maximum196844
Range196842
Interquartile range (IQR)1147.75

Descriptive statistics

Standard deviation6074.95781
Coefficient of variation (CV)3.576371689
Kurtosis438.2477252
Mean1698.637149
Median Absolute Deviation (MAD)451.5
Skewness17.33182959
Sum4718814
Variance36905112.4
MonotonicityNot monotonic
2021-10-16T19:44:38.132720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
1508
 
0.3%
2468
 
0.3%
4937
 
0.3%
2007
 
0.3%
5167
 
0.3%
2197
 
0.3%
3007
 
0.3%
12007
 
0.3%
2607
 
0.3%
Other values (1629)2702
97.3%
ValueCountFrequency (%)
21
< 0.1%
161
< 0.1%
171
< 0.1%
191
< 0.1%
201
< 0.1%
251
< 0.1%
272
0.1%
301
< 0.1%
321
< 0.1%
332
0.1%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2776
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.29020617
Minimum2.150588235
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-16T19:44:38.227930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.853695528
Q112.44787992
median17.94687607
Q325.01903646
95-th percentile88.33747541
Maximum56157.5
Range56155.34941
Interquartile range (IQR)12.57115653

Descriptive statistics

Standard deviation1070.278261
Coefficient of variation (CV)20.46804438
Kurtosis2722.240036
Mean52.29020617
Median Absolute Deviation (MAD)6.320760629
Skewness51.93815733
Sum145262.1927
Variance1145495.555
MonotonicityNot monotonic
2021-10-16T19:44:38.315222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1622
 
0.1%
14.478333332
 
0.1%
18.152222221
 
< 0.1%
44.627692311
 
< 0.1%
19.030483871
 
< 0.1%
28.554516131
 
< 0.1%
12.800681821
 
< 0.1%
6.3962146891
 
< 0.1%
26.087971011
 
< 0.1%
17.984615381
 
< 0.1%
Other values (2766)2766
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
56157.51
< 0.1%
4453.431
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%
615.751
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1155
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.77319647
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-16T19:44:38.407312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134.24166667
median59
Q399
95-th percentile224
Maximum366
Range365
Interquartile range (IQR)64.75833333

Descriptive statistics

Standard deviation66.49167413
Coefficient of variation (CV)0.8440900853
Kurtosis3.677747083
Mean78.77319647
Median Absolute Deviation (MAD)30
Skewness1.828783885
Sum218831.9398
Variance4421.142728
MonotonicityNot monotonic
2021-10-16T19:44:38.502692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7021
 
0.8%
4618
 
0.6%
5517
 
0.6%
3116
 
0.6%
4916
 
0.6%
9116
 
0.6%
2115
 
0.5%
4215
 
0.5%
3515
 
0.5%
1414
 
0.5%
Other values (1145)2615
94.1%
ValueCountFrequency (%)
19
0.3%
24
0.1%
2.8615384621
 
< 0.1%
36
0.2%
3.3303571431
 
< 0.1%
3.3513513511
 
< 0.1%
45
0.2%
4.1910112361
 
< 0.1%
4.2758620691
 
< 0.1%
4.51
 
< 0.1%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3641
 
< 0.1%
3631
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

freq_purchase
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1225
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04968006571
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-16T19:44:38.600578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008746355685
Q10.01575839204
median0.0243902439
Q30.04166666667
95-th percentile0.1153846154
Maximum17
Range16.99455041
Interquartile range (IQR)0.02590827462

Descriptive statistics

Standard deviation0.3373526206
Coefficient of variation (CV)6.790502704
Kurtosis2299.815337
Mean0.04968006571
Median Absolute Deviation (MAD)0.01069454458
Skewness46.11842612
Sum138.0112225
Variance0.1138067906
MonotonicityNot monotonic
2021-10-16T19:44:38.693244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.062518
 
0.6%
0.0277777777817
 
0.6%
0.0238095238116
 
0.6%
0.0833333333315
 
0.5%
0.0909090909115
 
0.5%
0.0344827586215
 
0.5%
0.0294117647114
 
0.5%
0.0192307692313
 
0.5%
0.0256410256413
 
0.5%
0.0212765957413
 
0.5%
Other values (1215)2629
94.6%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%
1.1428571431
 
< 0.1%
18
0.3%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%
0.53
 
0.1%

qtd_returned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct205
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.06731461
Minimum0
Maximum80995
Zeros1484
Zeros (%)53.4%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-16T19:44:38.790358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile98
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1563.268304
Coefficient of variation (CV)24.40040312
Kurtosis2589.980348
Mean64.06731461
Median Absolute Deviation (MAD)0
Skewness50.13804465
Sum177979
Variance2443807.789
MonotonicityNot monotonic
2021-10-16T19:44:38.885444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01484
53.4%
1129
 
4.6%
2118
 
4.2%
382
 
3.0%
472
 
2.6%
663
 
2.3%
555
 
2.0%
1245
 
1.6%
839
 
1.4%
938
 
1.4%
Other values (195)653
23.5%
ValueCountFrequency (%)
01484
53.4%
1129
 
4.6%
2118
 
4.2%
382
 
3.0%
472
 
2.6%
555
 
2.0%
663
 
2.3%
738
 
1.4%
839
 
1.4%
938
 
1.4%
ValueCountFrequency (%)
809951
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%

freq_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct424
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2606695734
Minimum0
Maximum4
Zeros1484
Zeros (%)53.4%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-16T19:44:38.984300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.3214285714
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)0.3214285714

Descriptive statistics

Standard deviation0.4446837808
Coefficient of variation (CV)1.705928985
Kurtosis2.082979121
Mean0.2606695734
Median Absolute Deviation (MAD)0
Skewness1.48088856
Sum724.140075
Variance0.1977436649
MonotonicityNot monotonic
2021-10-16T19:44:39.082606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01484
53.4%
1666
24.0%
210
 
0.4%
0.025641025647
 
0.3%
0.57
 
0.3%
0.28571428577
 
0.3%
0.256
 
0.2%
0.0094786729865
 
0.2%
0.22222222225
 
0.2%
0.019607843145
 
0.2%
Other values (414)576
 
20.7%
ValueCountFrequency (%)
01484
53.4%
0.0055710306411
 
< 0.1%
0.0056818181821
 
< 0.1%
0.0058651026391
 
< 0.1%
0.0059347181011
 
< 0.1%
0.0059523809521
 
< 0.1%
0.0060240963861
 
< 0.1%
0.0060422960731
 
< 0.1%
0.0061728395061
 
< 0.1%
0.0061919504641
 
< 0.1%
ValueCountFrequency (%)
41
 
< 0.1%
31
 
< 0.1%
210
 
0.4%
1666
24.0%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.57
 
0.3%
0.42857142861
 
< 0.1%
0.44
 
0.1%
0.33333333331
 
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1940
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.9312091
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-16T19:44:39.184052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45
Q1103.3333333
median172.125
Q3278.2375
95-th percentile587.6607143
Maximum40498.5
Range40497.5
Interquartile range (IQR)174.9041667

Descriptive statistics

Standard deviation807.5051928
Coefficient of variation (CV)3.283459613
Kurtosis2226.49079
Mean245.9312091
Median Absolute Deviation (MAD)81.125
Skewness44.89224702
Sum683196.899
Variance652064.6364
MonotonicityNot monotonic
2021-10-16T19:44:39.278940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
869
 
0.3%
758
 
0.3%
608
 
0.3%
1367
 
0.3%
1057
 
0.3%
827
 
0.3%
737
 
0.3%
2087
 
0.3%
1977
 
0.3%
Other values (1930)2700
97.2%
ValueCountFrequency (%)
11
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
11.8751
< 0.1%
ValueCountFrequency (%)
40498.51
< 0.1%
6009.3333331
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%
2082.2258061
< 0.1%
20001
< 0.1%
1903.51
< 0.1%

avg_basket_variety
Real number (ℝ≥0)

HIGH CORRELATION

Distinct897
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.13645973
Minimum0.2
Maximum177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-16T19:44:39.375925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.545454545
median13.5
Q322
95-th percentile45.0375
Maximum177
Range176.8
Interquartile range (IQR)14.45454545

Descriptive statistics

Standard deviation14.25495218
Coefficient of variation (CV)0.8318493087
Kurtosis10.01694329
Mean17.13645973
Median Absolute Deviation (MAD)6.666666667
Skewness2.246676666
Sum47605.08512
Variance203.2036616
MonotonicityNot monotonic
2021-10-16T19:44:39.470956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
834
 
1.2%
1333
 
1.2%
932
 
1.2%
1632
 
1.2%
732
 
1.2%
1230
 
1.1%
1429
 
1.0%
629
 
1.0%
1729
 
1.0%
18.529
 
1.0%
Other values (887)2469
88.9%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333336
0.2%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.4%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
1771
< 0.1%
1051
< 0.1%
1041
< 0.1%
981
< 0.1%
95.51
< 0.1%
94.333333331
< 0.1%
93.333333331
< 0.1%
89.6251
< 0.1%
871
< 0.1%
85.666666671
< 0.1%

Interactions

2021-10-16T19:44:35.050705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:18.327452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:19.511867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:20.759713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:21.952604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:23.315454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:24.601958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:25.987337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:27.234247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:28.439231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:29.826890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:31.070263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:32.332017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:33.776825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:35.136437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:18.433090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:19.589818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:20.838217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:22.038636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:23.402297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:24.689294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:26.071449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:27.320313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:28.523432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:29.911176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:31.155842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:32.418330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:33.864230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:35.221493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:18.514634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:19.668402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:20.920132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:22.125302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:23.490381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:24.775933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:26.155965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:27.404720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:28.607301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:29.995816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:31.242826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:32.504497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:33.950541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:35.306863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:18.593052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:19.747300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:21.006588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:22.209822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:23.577327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:24.863260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:26.239283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:27.488183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:28.690912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:30.083268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:31.329247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:32.590193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:34.034199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:35.395480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:18.676127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:19.830482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:21.092328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:22.300133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:23.671697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:24.954054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:26.327954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:27.572682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:28.922793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:30.171006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:31.420438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:32.678939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:34.124685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:35.486904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:18.760688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:19.916058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:21.177007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:22.391301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:23.766885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:25.046518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:26.417600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:27.660829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:29.010907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:30.261458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:31.513418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:32.771920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:34.217522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:35.577706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:18.846123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:20.001283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:21.262585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:22.579944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:23.860948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:25.138488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:26.508242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:27.749238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:29.101303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:30.353439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:31.607577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:32.862930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:34.310830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:35.669624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:18.930911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:20.088080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:21.348181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:22.678249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:23.955442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:25.232813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:26.599669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:27.837939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:29.193370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:30.443760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:31.700243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:32.955864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:34.406396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:35.753665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:19.009716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:20.246951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:21.426921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:22.761245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:24.043274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:25.435463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:26.688343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:27.919039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:29.278090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:30.526741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:31.785577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:33.217243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:34.492562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:35.842846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:19.091887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:20.330629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:21.510512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:22.853467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:24.134192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:25.523680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:26.779314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:28.002870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:29.367213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:30.615008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:31.874476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:33.307616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:34.584826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:35.931924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:19.174982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:20.415329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:21.594439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:22.943914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:24.225826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:25.613848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:26.868504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:28.089274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:29.459176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:30.704231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:31.963744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:33.397917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:34.677466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:36.023485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:19.259750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:20.501919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:21.681059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:23.038040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:24.319717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:25.706847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:26.959558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:28.177573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:29.550775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:30.794512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:32.056740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:33.492192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:34.771424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:36.114290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:19.344389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:20.588776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:21.767535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:23.129795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:24.413085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:25.800937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:27.050577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:28.266406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:29.642772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:30.885915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:32.148130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:33.585511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:34.864516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:36.206411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:19.429716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:20.674989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:21.853876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:23.223525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:24.508173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:25.895432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:27.145123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:28.354360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:29.737501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:30.976424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:32.242058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:33.682113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-16T19:44:34.959094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-16T19:44:39.565237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-16T19:44:39.700567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-16T19:44:39.835202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-16T19:44:39.970617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-16T19:44:36.374172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-16T19:44:36.553374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqnt_purchasesvar_productsqnt_itemsavg_ticketavg_recency_daysfreq_purchaseqtd_returnedfreq_returnsavg_basket_sizeavg_basket_variety
00178505391.21372.034.0297.01733.018.1522221.00000017.00000040.01.00000050.9705880.617647
11130473232.5956.09.0171.01390.018.90403552.8333330.02830235.00.023973154.44444411.666667
22125836705.382.015.0232.05028.028.90250026.5000000.04032350.00.105263335.2000007.600000
3313748948.2595.05.028.0439.033.86607192.6666670.0179210.00.00000087.8000004.800000
4415100876.00333.03.03.080.0292.00000020.0000000.07317122.00.07894726.6666670.333333
55152914623.3025.014.0102.02102.045.32647126.7692310.04011529.00.032468150.1428574.357143
66146885630.877.021.0327.03621.017.21978619.2631580.057221399.00.019608172.4285717.047619
77178095411.9116.012.061.02057.088.71983639.6666670.03352041.00.013072171.4166673.833333
881531160767.900.091.02379.038194.025.5434644.1910110.243316474.00.072193419.7142866.230769
99160982005.6387.07.067.0613.029.93477647.6666670.0243900.00.00000087.5714294.857143

Last rows

df_indexcustomer_idgross_revenuerecency_daysqnt_purchasesvar_productsqnt_itemsavg_ticketavg_recency_daysfreq_purchaseqtd_returnedfreq_returnsavg_basket_sizeavg_basket_variety
2768562017290525.243.02.0102.0404.05.14941213.00.1428570.00.0202.00000046.000000
276956291478577.4010.02.03.084.025.8000005.00.3333330.00.042.0000001.000000
2770563017254272.444.02.0112.0252.02.43250011.00.1666670.00.0126.00000050.000000
2771564717232421.522.02.036.0203.011.70888912.00.1538460.00.0101.50000015.000000
2772564817468137.0010.02.05.0116.027.4000004.00.4000000.00.058.0000002.500000
2773565913596697.045.02.0166.0406.04.1990367.00.2500000.00.0203.00000066.500000
27745665148931237.859.02.073.0799.016.9568492.00.6666670.00.0399.50000036.000000
2775569014126706.137.03.015.0508.047.0753333.00.75000050.01.0169.3333334.666667
27765696135211092.391.03.0435.0733.02.5112414.50.3000000.00.0244.333333104.000000
2777570615060301.848.04.0120.0262.02.5153331.02.0000000.00.065.50000020.000000